Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations6500
Missing cells5223
Missing cells (%)4.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1015.8 KiB
Average record size in memory160.0 B

Variable types

Text3
Numeric7
Categorical3
Boolean5
DateTime2

Alerts

Base Shipping Price is highly overall correlated with Cost and 2 other fieldsHigh correlation
Cost is highly overall correlated with Base Shipping Price and 2 other fieldsHigh correlation
Express Shipment is highly overall correlated with TransportHigh correlation
Fragile is highly overall correlated with MaterialHigh correlation
Height is highly overall correlated with Weight and 1 other fieldsHigh correlation
International is highly overall correlated with TransportHigh correlation
Material is highly overall correlated with FragileHigh correlation
Price Of Sculpture is highly overall correlated with Base Shipping Price and 2 other fieldsHigh correlation
Transport is highly overall correlated with Express Shipment and 1 other fieldsHigh correlation
Weight is highly overall correlated with Base Shipping Price and 4 other fieldsHigh correlation
Width is highly overall correlated with Height and 1 other fieldsHigh correlation
Artist Reputation has 750 (11.5%) missing values Missing
Height has 375 (5.8%) missing values Missing
Width has 584 (9.0%) missing values Missing
Weight has 587 (9.0%) missing values Missing
Material has 764 (11.8%) missing values Missing
Transport has 1392 (21.4%) missing values Missing
Remote Location has 771 (11.9%) missing values Missing
Weight is highly skewed (γ1 = 21.55617439) Skewed
Price Of Sculpture is highly skewed (γ1 = 22.20682308) Skewed
Cost is highly skewed (γ1 = 29.81745934) Skewed
Customer Id has unique values Unique
Customer Location has unique values Unique

Reproduction

Analysis started2024-11-09 15:21:00.919578
Analysis finished2024-11-09 15:21:23.731150
Duration22.81 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Customer Id
Text

Unique 

Distinct6500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size50.9 KiB
2024-11-09T15:21:24.036583image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length24
Median length20
Mean length19.561231
Min length8

Characters and Unicode

Total characters127148
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6500 ?
Unique (%)100.0%

Sample

1st rowfffe3900350033003300
2nd rowfffe3800330031003900
3rd rowfffe3600370035003100
4th rowfffe350031003300
5th rowfffe3900320038003400
ValueCountFrequency (%)
fffe3900350033003300 1
 
< 0.1%
fffe3900370032003400 1
 
< 0.1%
fffe350031003300 1
 
< 0.1%
fffe3900320038003400 1
 
< 0.1%
fffe3300390039003900 1
 
< 0.1%
fffe3800360033003700 1
 
< 0.1%
fffe3800300039003800 1
 
< 0.1%
fffe3800330032003900 1
 
< 0.1%
fffe3800310031003800 1
 
< 0.1%
fffe3100350034003800 1
 
< 0.1%
Other values (6490) 6490
99.8%
2024-11-09T15:21:24.790400image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 52504
41.3%
3 27862
21.9%
f 19500
 
15.3%
e 6500
 
5.1%
6 2648
 
2.1%
2 2628
 
2.1%
1 2620
 
2.1%
9 2604
 
2.0%
8 2593
 
2.0%
4 2575
 
2.0%
Other values (2) 5114
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 127148
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 52504
41.3%
3 27862
21.9%
f 19500
 
15.3%
e 6500
 
5.1%
6 2648
 
2.1%
2 2628
 
2.1%
1 2620
 
2.1%
9 2604
 
2.0%
8 2593
 
2.0%
4 2575
 
2.0%
Other values (2) 5114
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 127148
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 52504
41.3%
3 27862
21.9%
f 19500
 
15.3%
e 6500
 
5.1%
6 2648
 
2.1%
2 2628
 
2.1%
1 2620
 
2.1%
9 2604
 
2.0%
8 2593
 
2.0%
4 2575
 
2.0%
Other values (2) 5114
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 127148
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 52504
41.3%
3 27862
21.9%
f 19500
 
15.3%
e 6500
 
5.1%
6 2648
 
2.1%
2 2628
 
2.1%
1 2620
 
2.1%
9 2604
 
2.0%
8 2593
 
2.0%
4 2575
 
2.0%
Other values (2) 5114
 
4.0%
Distinct6449
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size50.9 KiB
2024-11-09T15:21:25.317017image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length22
Median length20
Mean length13.068
Min length7

Characters and Unicode

Total characters84942
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6403 ?
Unique (%)98.5%

Sample

1st rowBilly Jenkins
2nd rowJean Bryant
3rd rowLaura Miller
4th rowRobert Chaires
5th rowRosalyn Krol
ValueCountFrequency (%)
james 139
 
1.1%
robert 117
 
0.9%
john 102
 
0.8%
mary 100
 
0.8%
william 89
 
0.7%
thomas 87
 
0.7%
david 79
 
0.6%
michael 77
 
0.6%
smith 70
 
0.5%
johnson 61
 
0.5%
Other values (5061) 12079
92.9%
2024-11-09T15:21:26.176925image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 8389
 
9.9%
a 7771
 
9.1%
6500
 
7.7%
r 6319
 
7.4%
n 5979
 
7.0%
i 5071
 
6.0%
o 4726
 
5.6%
l 4669
 
5.5%
s 3278
 
3.9%
t 3075
 
3.6%
Other values (43) 29165
34.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 84942
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 8389
 
9.9%
a 7771
 
9.1%
6500
 
7.7%
r 6319
 
7.4%
n 5979
 
7.0%
i 5071
 
6.0%
o 4726
 
5.6%
l 4669
 
5.5%
s 3278
 
3.9%
t 3075
 
3.6%
Other values (43) 29165
34.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 84942
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 8389
 
9.9%
a 7771
 
9.1%
6500
 
7.7%
r 6319
 
7.4%
n 5979
 
7.0%
i 5071
 
6.0%
o 4726
 
5.6%
l 4669
 
5.5%
s 3278
 
3.9%
t 3075
 
3.6%
Other values (43) 29165
34.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 84942
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 8389
 
9.9%
a 7771
 
9.1%
6500
 
7.7%
r 6319
 
7.4%
n 5979
 
7.0%
i 5071
 
6.0%
o 4726
 
5.6%
l 4669
 
5.5%
s 3278
 
3.9%
t 3075
 
3.6%
Other values (43) 29165
34.3%

Artist Reputation
Real number (ℝ)

Missing 

Distinct101
Distinct (%)1.8%
Missing750
Missing (%)11.5%
Infinite0
Infinite (%)0.0%
Mean0.46185043
Minimum0
Maximum1
Zeros22
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-11-09T15:21:26.557048image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.06
Q10.24
median0.45
Q30.68
95-th percentile0.91
Maximum1
Range1
Interquartile range (IQR)0.44

Descriptive statistics

Standard deviation0.26578113
Coefficient of variation (CV)0.57547013
Kurtosis-1.0318797
Mean0.46185043
Median Absolute Deviation (MAD)0.22
Skewness0.1413632
Sum2655.64
Variance0.070639609
MonotonicityNot monotonic
2024-11-09T15:21:26.901737image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.36 90
 
1.4%
0.38 85
 
1.3%
0.22 79
 
1.2%
0.39 79
 
1.2%
0.17 79
 
1.2%
0.47 77
 
1.2%
0.37 77
 
1.2%
0.29 76
 
1.2%
0.6 75
 
1.2%
0.57 74
 
1.1%
Other values (91) 4959
76.3%
(Missing) 750
 
11.5%
ValueCountFrequency (%)
0 22
 
0.3%
0.01 57
0.9%
0.02 60
0.9%
0.03 36
0.6%
0.04 45
0.7%
0.05 57
0.9%
0.06 54
0.8%
0.07 56
0.9%
0.08 64
1.0%
0.09 52
0.8%
ValueCountFrequency (%)
1 12
 
0.2%
0.99 29
0.4%
0.98 25
0.4%
0.97 35
0.5%
0.96 32
0.5%
0.95 27
0.4%
0.94 41
0.6%
0.93 35
0.5%
0.92 36
0.6%
0.91 31
0.5%

Height
Real number (ℝ)

High correlation  Missing 

Distinct65
Distinct (%)1.1%
Missing375
Missing (%)5.8%
Infinite0
Infinite (%)0.0%
Mean21.766204
Minimum3
Maximum73
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-11-09T15:21:27.227009image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q112
median20
Q330
95-th percentile44
Maximum73
Range70
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.968192
Coefficient of variation (CV)0.54985206
Kurtosis-0.0052948465
Mean21.766204
Median Absolute Deviation (MAD)8
Skewness0.59404059
Sum133318
Variance143.23762
MonotonicityNot monotonic
2024-11-09T15:21:27.584491image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 211
 
3.2%
19 209
 
3.2%
15 204
 
3.1%
16 192
 
3.0%
13 191
 
2.9%
18 191
 
2.9%
26 183
 
2.8%
9 180
 
2.8%
22 180
 
2.8%
20 180
 
2.8%
Other values (55) 4204
64.7%
(Missing) 375
 
5.8%
ValueCountFrequency (%)
3 148
2.3%
4 129
2.0%
5 135
2.1%
6 129
2.0%
7 141
2.2%
8 167
2.6%
9 180
2.8%
10 170
2.6%
11 148
2.3%
12 211
3.2%
ValueCountFrequency (%)
73 1
 
< 0.1%
68 2
 
< 0.1%
66 2
 
< 0.1%
65 1
 
< 0.1%
64 5
0.1%
62 2
 
< 0.1%
61 4
0.1%
60 3
< 0.1%
59 3
< 0.1%
58 7
0.1%

Width
Real number (ℝ)

High correlation  Missing 

Distinct40
Distinct (%)0.7%
Missing584
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean9.6176471
Minimum2
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-11-09T15:21:27.917797image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q16
median8
Q312
95-th percentile21
Maximum50
Range48
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.4170002
Coefficient of variation (CV)0.5632355
Kurtosis3.3617162
Mean9.6176471
Median Absolute Deviation (MAD)3
Skewness1.5467021
Sum56898
Variance29.343891
MonotonicityNot monotonic
2024-11-09T15:21:28.240885image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
8 741
11.4%
6 722
11.1%
7 698
10.7%
4 473
 
7.3%
5 435
 
6.7%
9 347
 
5.3%
10 326
 
5.0%
11 311
 
4.8%
12 261
 
4.0%
13 207
 
3.2%
Other values (30) 1395
21.5%
(Missing) 584
9.0%
ValueCountFrequency (%)
2 76
 
1.2%
3 164
 
2.5%
4 473
7.3%
5 435
6.7%
6 722
11.1%
7 698
10.7%
8 741
11.4%
9 347
5.3%
10 326
5.0%
11 311
4.8%
ValueCountFrequency (%)
50 1
 
< 0.1%
45 1
 
< 0.1%
44 1
 
< 0.1%
39 1
 
< 0.1%
37 3
< 0.1%
36 3
< 0.1%
35 2
 
< 0.1%
34 3
< 0.1%
33 6
0.1%
32 5
0.1%

Weight
Real number (ℝ)

High correlation  Missing  Skewed 

Distinct4410
Distinct (%)74.6%
Missing587
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean400694.82
Minimum3
Maximum1.1792787 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-11-09T15:21:28.564763image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile63.6
Q1503
median3102
Q336456
95-th percentile1430595.4
Maximum1.1792787 × 108
Range1.1792787 × 108
Interquartile range (IQR)35953

Descriptive statistics

Standard deviation2678081.2
Coefficient of variation (CV)6.6835933
Kurtosis731.84356
Mean400694.82
Median Absolute Deviation (MAD)2992
Skewness21.556174
Sum2.3693085 × 109
Variance7.1721191 × 1012
MonotonicityNot monotonic
2024-11-09T15:21:28.911020image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58 12
 
0.2%
237 10
 
0.2%
84 10
 
0.2%
41 10
 
0.2%
33 9
 
0.1%
90 9
 
0.1%
220 9
 
0.1%
39 9
 
0.1%
112 9
 
0.1%
11 8
 
0.1%
Other values (4400) 5818
89.5%
(Missing) 587
 
9.0%
ValueCountFrequency (%)
3 1
 
< 0.1%
4 3
 
< 0.1%
5 3
 
< 0.1%
6 3
 
< 0.1%
8 3
 
< 0.1%
10 6
0.1%
11 8
0.1%
12 3
 
< 0.1%
13 8
0.1%
14 5
0.1%
ValueCountFrequency (%)
117927869 1
< 0.1%
53118478 1
< 0.1%
49145353 1
< 0.1%
47717848 1
< 0.1%
47544217 1
< 0.1%
44587296 1
< 0.1%
29282087 1
< 0.1%
27317777 1
< 0.1%
26793635 1
< 0.1%
25254560 1
< 0.1%

Material
Categorical

High correlation  Missing 

Distinct7
Distinct (%)0.1%
Missing764
Missing (%)11.8%
Memory size50.9 KiB
Brass
847 
Aluminium
845 
Bronze
821 
Marble
819 
Clay
816 
Other values (2)
1588 

Length

Max length9
Median length6
Mean length5.5906555
Min length4

Characters and Unicode

Total characters32068
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBrass
2nd rowBrass
3rd rowClay
4th rowAluminium
5th rowAluminium

Common Values

ValueCountFrequency (%)
Brass 847
13.0%
Aluminium 845
13.0%
Bronze 821
12.6%
Marble 819
12.6%
Clay 816
12.6%
Wood 816
12.6%
Stone 772
11.9%
(Missing) 764
11.8%

Length

2024-11-09T15:21:29.226704image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:21:29.500266image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
brass 847
14.8%
aluminium 845
14.7%
bronze 821
14.3%
marble 819
14.3%
clay 816
14.2%
wood 816
14.2%
stone 772
13.5%

Most occurring characters

ValueCountFrequency (%)
o 3225
 
10.1%
r 2487
 
7.8%
a 2482
 
7.7%
l 2480
 
7.7%
n 2438
 
7.6%
e 2412
 
7.5%
s 1694
 
5.3%
u 1690
 
5.3%
m 1690
 
5.3%
i 1690
 
5.3%
Other values (11) 9780
30.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 32068
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 3225
 
10.1%
r 2487
 
7.8%
a 2482
 
7.7%
l 2480
 
7.7%
n 2438
 
7.6%
e 2412
 
7.5%
s 1694
 
5.3%
u 1690
 
5.3%
m 1690
 
5.3%
i 1690
 
5.3%
Other values (11) 9780
30.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 32068
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 3225
 
10.1%
r 2487
 
7.8%
a 2482
 
7.7%
l 2480
 
7.7%
n 2438
 
7.6%
e 2412
 
7.5%
s 1694
 
5.3%
u 1690
 
5.3%
m 1690
 
5.3%
i 1690
 
5.3%
Other values (11) 9780
30.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 32068
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 3225
 
10.1%
r 2487
 
7.8%
a 2482
 
7.7%
l 2480
 
7.7%
n 2438
 
7.6%
e 2412
 
7.5%
s 1694
 
5.3%
u 1690
 
5.3%
m 1690
 
5.3%
i 1690
 
5.3%
Other values (11) 9780
30.5%

Price Of Sculpture
Real number (ℝ)

High correlation  Skewed 

Distinct3424
Distinct (%)52.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1192.4201
Minimum3
Maximum382385.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-11-09T15:21:29.850398image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3.44
Q15.23
median8.025
Q389.47
95-th percentile3631.7655
Maximum382385.67
Range382382.67
Interquartile range (IQR)84.24

Descriptive statistics

Standard deviation8819.6168
Coefficient of variation (CV)7.3964007
Kurtosis727.30112
Mean1192.4201
Median Absolute Deviation (MAD)4.515
Skewness22.206823
Sum7750730.6
Variance77785640
MonotonicityNot monotonic
2024-11-09T15:21:30.210432image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.27 19
 
0.3%
3.82 16
 
0.2%
5.1 16
 
0.2%
6.58 16
 
0.2%
5.54 16
 
0.2%
5.31 15
 
0.2%
4.89 14
 
0.2%
6.1 14
 
0.2%
3.06 14
 
0.2%
6.61 14
 
0.2%
Other values (3414) 6346
97.6%
ValueCountFrequency (%)
3 6
0.1%
3.01 11
0.2%
3.02 12
0.2%
3.03 6
0.1%
3.04 9
0.1%
3.05 5
 
0.1%
3.06 14
0.2%
3.07 6
0.1%
3.08 7
0.1%
3.09 4
 
0.1%
ValueCountFrequency (%)
382385.67 1
< 0.1%
231660.46 1
< 0.1%
230578.77 1
< 0.1%
162583.13 1
< 0.1%
135878.04 1
< 0.1%
114355.43 1
< 0.1%
105746.47 1
< 0.1%
102245.98 1
< 0.1%
98670.21 1
< 0.1%
88827.64 1
< 0.1%

Base Shipping Price
Real number (ℝ)

High correlation 

Distinct3732
Distinct (%)57.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.407174
Minimum10
Maximum99.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-11-09T15:21:30.548234image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile11.33
Q116.7
median23.505
Q357.905
95-th percentile91.5805
Maximum99.98
Range89.98
Interquartile range (IQR)41.205

Descriptive statistics

Standard deviation26.873519
Coefficient of variation (CV)0.71840548
Kurtosis-0.57931572
Mean37.407174
Median Absolute Deviation (MAD)10.475
Skewness0.91810238
Sum243146.63
Variance722.186
MonotonicityNot monotonic
2024-11-09T15:21:31.171733image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.5 10
 
0.2%
10.68 9
 
0.1%
19.46 8
 
0.1%
12.89 7
 
0.1%
22.96 7
 
0.1%
17.95 7
 
0.1%
20.78 7
 
0.1%
24.87 7
 
0.1%
18.32 7
 
0.1%
14.65 7
 
0.1%
Other values (3722) 6424
98.8%
ValueCountFrequency (%)
10 3
< 0.1%
10.01 2
< 0.1%
10.03 3
< 0.1%
10.04 2
< 0.1%
10.05 1
 
< 0.1%
10.06 3
< 0.1%
10.07 3
< 0.1%
10.08 2
< 0.1%
10.09 2
< 0.1%
10.1 2
< 0.1%
ValueCountFrequency (%)
99.98 1
< 0.1%
99.95 1
< 0.1%
99.91 1
< 0.1%
99.81 1
< 0.1%
99.8 1
< 0.1%
99.76 1
< 0.1%
99.72 1
< 0.1%
99.71 2
< 0.1%
99.64 1
< 0.1%
99.61 1
< 0.1%

International
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
False
4294 
True
2206 
ValueCountFrequency (%)
False 4294
66.1%
True 2206
33.9%
2024-11-09T15:21:31.538484image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Express Shipment
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
False
4365 
True
2135 
ValueCountFrequency (%)
False 4365
67.2%
True 2135
32.8%
2024-11-09T15:21:31.883319image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
False
3916 
True
2584 
ValueCountFrequency (%)
False 3916
60.2%
True 2584
39.8%
2024-11-09T15:21:32.182757image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Transport
Categorical

High correlation  Missing 

Distinct3
Distinct (%)0.1%
Missing1392
Missing (%)21.4%
Memory size50.9 KiB
Roadways
2064 
Airways
1817 
Waterways
1227 

Length

Max length9
Median length8
Mean length7.8844949
Min length7

Characters and Unicode

Total characters40274
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAirways
2nd rowRoadways
3rd rowRoadways
4th rowAirways
5th rowRoadways

Common Values

ValueCountFrequency (%)
Roadways 2064
31.8%
Airways 1817
28.0%
Waterways 1227
18.9%
(Missing) 1392
21.4%

Length

2024-11-09T15:21:32.528882image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:21:32.818443image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
roadways 2064
40.4%
airways 1817
35.6%
waterways 1227
24.0%

Most occurring characters

ValueCountFrequency (%)
a 8399
20.9%
w 5108
12.7%
y 5108
12.7%
s 5108
12.7%
r 3044
 
7.6%
R 2064
 
5.1%
o 2064
 
5.1%
d 2064
 
5.1%
A 1817
 
4.5%
i 1817
 
4.5%
Other values (3) 3681
9.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 40274
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 8399
20.9%
w 5108
12.7%
y 5108
12.7%
s 5108
12.7%
r 3044
 
7.6%
R 2064
 
5.1%
o 2064
 
5.1%
d 2064
 
5.1%
A 1817
 
4.5%
i 1817
 
4.5%
Other values (3) 3681
9.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 40274
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 8399
20.9%
w 5108
12.7%
y 5108
12.7%
s 5108
12.7%
r 3044
 
7.6%
R 2064
 
5.1%
o 2064
 
5.1%
d 2064
 
5.1%
A 1817
 
4.5%
i 1817
 
4.5%
Other values (3) 3681
9.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 40274
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 8399
20.9%
w 5108
12.7%
y 5108
12.7%
s 5108
12.7%
r 3044
 
7.6%
R 2064
 
5.1%
o 2064
 
5.1%
d 2064
 
5.1%
A 1817
 
4.5%
i 1817
 
4.5%
Other values (3) 3681
9.1%

Fragile
Boolean

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
False
5461 
True
1039 
ValueCountFrequency (%)
False 5461
84.0%
True 1039
 
16.0%
2024-11-09T15:21:33.220951image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.9 KiB
Working Class
4803 
Wealthy
1697 

Length

Max length13
Median length13
Mean length11.433538
Min length7

Characters and Unicode

Total characters74318
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowWorking Class
2nd rowWorking Class
3rd rowWorking Class
4th rowWealthy
5th rowWorking Class

Common Values

ValueCountFrequency (%)
Working Class 4803
73.9%
Wealthy 1697
 
26.1%

Length

2024-11-09T15:21:33.695467image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-09T15:21:34.150596image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
working 4803
42.5%
class 4803
42.5%
wealthy 1697
 
15.0%

Most occurring characters

ValueCountFrequency (%)
s 9606
12.9%
W 6500
 
8.7%
l 6500
 
8.7%
a 6500
 
8.7%
o 4803
 
6.5%
r 4803
 
6.5%
k 4803
 
6.5%
i 4803
 
6.5%
n 4803
 
6.5%
g 4803
 
6.5%
Other values (6) 16394
22.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 74318
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 9606
12.9%
W 6500
 
8.7%
l 6500
 
8.7%
a 6500
 
8.7%
o 4803
 
6.5%
r 4803
 
6.5%
k 4803
 
6.5%
i 4803
 
6.5%
n 4803
 
6.5%
g 4803
 
6.5%
Other values (6) 16394
22.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 74318
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 9606
12.9%
W 6500
 
8.7%
l 6500
 
8.7%
a 6500
 
8.7%
o 4803
 
6.5%
r 4803
 
6.5%
k 4803
 
6.5%
i 4803
 
6.5%
n 4803
 
6.5%
g 4803
 
6.5%
Other values (6) 16394
22.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 74318
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 9606
12.9%
W 6500
 
8.7%
l 6500
 
8.7%
a 6500
 
8.7%
o 4803
 
6.5%
r 4803
 
6.5%
k 4803
 
6.5%
i 4803
 
6.5%
n 4803
 
6.5%
g 4803
 
6.5%
Other values (6) 16394
22.1%

Remote Location
Boolean

Missing 

Distinct2
Distinct (%)< 0.1%
Missing771
Missing (%)11.9%
Memory size12.8 KiB
False
4594 
True
1135 
(Missing)
771 
ValueCountFrequency (%)
False 4594
70.7%
True 1135
 
17.5%
(Missing) 771
 
11.9%
2024-11-09T15:21:34.566937image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Distinct1660
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Memory size50.9 KiB
Minimum2015-01-01 00:00:00
Maximum2019-08-27 00:00:00
2024-11-09T15:21:34.922408image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:35.256992image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct1664
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Memory size50.9 KiB
Minimum2015-01-01 00:00:00
Maximum2019-08-29 00:00:00
2024-11-09T15:21:35.564756image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:35.922802image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Customer Location
Text

Unique 

Distinct6500
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size50.9 KiB
2024-11-09T15:21:36.395001image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length34
Median length31
Mean length20.864615
Min length12

Characters and Unicode

Total characters135620
Distinct characters64
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6500 ?
Unique (%)100.0%

Sample

1st rowNew Michelle, OH 50777
2nd rowNew Michaelport, WY 12072
3rd rowBowmanshire, WA 19241
4th rowEast Robyn, KY 86375
5th rowAprilside, PA 52793
ValueCountFrequency (%)
new 417
 
1.9%
port 413
 
1.8%
south 410
 
1.8%
east 406
 
1.8%
lake 400
 
1.8%
north 396
 
1.8%
west 384
 
1.7%
ap 250
 
1.1%
aa 245
 
1.1%
dpo 244
 
1.1%
Other values (10158) 18761
84.0%
2024-11-09T15:21:37.198064image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15826
 
11.7%
e 6574
 
4.8%
, 5783
 
4.3%
t 5455
 
4.0%
a 5437
 
4.0%
r 5411
 
4.0%
o 4752
 
3.5%
h 3814
 
2.8%
n 3688
 
2.7%
i 3420
 
2.5%
Other values (54) 75460
55.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 135620
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15826
 
11.7%
e 6574
 
4.8%
, 5783
 
4.3%
t 5455
 
4.0%
a 5437
 
4.0%
r 5411
 
4.0%
o 4752
 
3.5%
h 3814
 
2.8%
n 3688
 
2.7%
i 3420
 
2.5%
Other values (54) 75460
55.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 135620
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15826
 
11.7%
e 6574
 
4.8%
, 5783
 
4.3%
t 5455
 
4.0%
a 5437
 
4.0%
r 5411
 
4.0%
o 4752
 
3.5%
h 3814
 
2.8%
n 3688
 
2.7%
i 3420
 
2.5%
Other values (54) 75460
55.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 135620
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15826
 
11.7%
e 6574
 
4.8%
, 5783
 
4.3%
t 5455
 
4.0%
a 5437
 
4.0%
r 5411
 
4.0%
o 4752
 
3.5%
h 3814
 
2.8%
n 3688
 
2.7%
i 3420
 
2.5%
Other values (54) 75460
55.6%

Cost
Real number (ℝ)

High correlation  Skewed 

Distinct6356
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17139.196
Minimum-880172.65
Maximum11143428
Zeros0
Zeros (%)0.0%
Negative659
Negative (%)10.1%
Memory size50.9 KiB
2024-11-09T15:21:37.550122image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum-880172.65
5-th percentile-432.166
Q1188.44
median382.065
Q31156.115
95-th percentile18543.878
Maximum11143428
Range12023601
Interquartile range (IQR)967.675

Descriptive statistics

Standard deviation240657.87
Coefficient of variation (CV)14.041375
Kurtosis1124.7715
Mean17139.196
Median Absolute Deviation (MAD)249.26
Skewness29.817459
Sum1.1140477 × 108
Variance5.791621 × 1010
MonotonicityNot monotonic
2024-11-09T15:21:37.892730image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
144.45 4
 
0.1%
479.27 3
 
< 0.1%
193.79 3
 
< 0.1%
269.99 3
 
< 0.1%
283.6 3
 
< 0.1%
191.55 3
 
< 0.1%
176.82 2
 
< 0.1%
349.03 2
 
< 0.1%
282.01 2
 
< 0.1%
915.1 2
 
< 0.1%
Other values (6346) 6473
99.6%
ValueCountFrequency (%)
-880172.65 1
< 0.1%
-588183.2 1
< 0.1%
-360814.49 1
< 0.1%
-322018.37 1
< 0.1%
-252965.99 1
< 0.1%
-244885.22 1
< 0.1%
-236681.06 1
< 0.1%
-162573.71 1
< 0.1%
-144567.29 1
< 0.1%
-133988.47 1
< 0.1%
ValueCountFrequency (%)
11143428.25 1
< 0.1%
9177540.38 1
< 0.1%
4992890.84 1
< 0.1%
4976100.89 1
< 0.1%
4020731.9 1
< 0.1%
3792938.06 1
< 0.1%
3362175.6 1
< 0.1%
3324867.29 1
< 0.1%
3117106.66 1
< 0.1%
2829193.76 1
< 0.1%

Interactions

2024-11-09T15:21:19.868456image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:08.368624image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:10.036086image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:11.744912image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:14.017806image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:15.730132image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:17.508546image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:20.242528image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:08.593695image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:10.277420image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:12.009618image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:14.248564image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:15.990338image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:17.766193image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:20.632602image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:08.861803image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:10.512336image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:12.254393image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:14.502858image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:16.238096image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:18.091347image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:21.038186image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:09.069648image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:10.755179image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:12.472623image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:14.755091image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:16.474820image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:18.416389image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:21.343300image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:09.303998image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:11.013309image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:12.707349image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:15.014503image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:16.753608image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:18.786727image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:21.586972image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:09.551936image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:11.271923image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:12.976127image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:15.266167image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:17.033282image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:19.154038image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:22.041124image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:09.804149image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:11.492596image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:13.781888image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:15.483824image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:17.262441image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-11-09T15:21:19.502511image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-11-09T15:21:38.140817image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Artist ReputationBase Shipping PriceCostCustomer InformationExpress ShipmentFragileHeightInstallation IncludedInternationalMaterialPrice Of SculptureRemote LocationTransportWeightWidth
Artist Reputation1.000-0.0030.3520.0160.0000.019-0.0000.0080.0270.0310.0910.0000.0390.009-0.011
Base Shipping Price-0.0031.0000.5940.0150.0200.1240.3740.0000.0000.2670.7030.0000.0000.7400.370
Cost0.3520.5941.0000.0000.0510.0000.3960.0330.0110.0660.6560.0000.0000.6610.387
Customer Information0.0160.0150.0001.0000.3680.0000.0000.0000.0000.0000.0000.0000.2070.0000.000
Express Shipment0.0000.0200.0510.3681.0000.0000.0200.0000.0100.0420.0180.0000.5520.0050.000
Fragile0.0190.1240.0000.0000.0001.0000.1310.0000.0000.9370.0140.0000.0000.0000.077
Height-0.0000.3740.3960.0000.0200.1311.0000.0280.0170.0070.4360.0000.0130.5580.826
Installation Included0.0080.0000.0330.0000.0000.0000.0281.0000.0130.0180.0150.0000.0180.0070.000
International0.0270.0000.0110.0000.0100.0000.0170.0131.0000.0000.0160.0000.5280.0000.000
Material0.0310.2670.0660.0000.0420.9370.0070.0180.0001.0000.0590.0000.0280.0690.007
Price Of Sculpture0.0910.7030.6560.0000.0180.0140.4360.0150.0160.0591.0000.0050.0000.8720.432
Remote Location0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.0051.0000.0000.0000.031
Transport0.0390.0000.0000.2070.5520.0000.0130.0180.5280.0280.0000.0001.0000.0040.000
Weight0.0090.7400.6610.0000.0050.0000.5580.0070.0000.0690.8720.0000.0041.0000.559
Width-0.0110.3700.3870.0000.0000.0770.8260.0000.0000.0070.4320.0310.0000.5591.000

Missing values

2024-11-09T15:21:22.412569image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-09T15:21:23.068209image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-09T15:21:23.513500image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Customer IdArtist NameArtist ReputationHeightWidthWeightMaterialPrice Of SculptureBase Shipping PriceInternationalExpress ShipmentInstallation IncludedTransportFragileCustomer InformationRemote LocationScheduled DateDelivery DateCustomer LocationCost
0fffe3900350033003300Billy Jenkins0.2617.06.04128.0Brass13.9116.27YesYesNoAirwaysNoWorking ClassNo06/07/1506/03/15New Michelle, OH 50777-283.29
1fffe3800330031003900Jean Bryant0.283.03.061.0Brass6.8315.00NoNoNoRoadwaysNoWorking ClassNo03/06/1703/05/17New Michaelport, WY 12072-159.96
2fffe3600370035003100Laura Miller0.078.05.0237.0Clay4.9621.18NoNoNoRoadwaysYesWorking ClassYes03/09/1503/08/15Bowmanshire, WA 19241-154.29
3fffe350031003300Robert Chaires0.129.0NaNNaNAluminium5.8116.31NoNoNoNaNNoWealthyYes05/24/1505/20/15East Robyn, KY 86375-161.16
4fffe3900320038003400Rosalyn Krol0.1517.06.0324.0Aluminium3.1811.94YesYesYesAirwaysNoWorking ClassNo12/18/1612/14/16Aprilside, PA 52793-159.23
5fffe3300390039003900Tracy Francis0.9946.019.01178.0Wood6.1616.88NoYesNoNaNNoWealthyNo08/28/1508/26/15Maddenberg, AL 43096-1922.78
6fffe3800360033003700David Hawes0.6417.09.07264.0Brass8.2690.67NoYesNoRoadwaysNoWorking ClassNo06/05/1606/02/16South Matthew, WV 76033-1536.66
7fffe3800300039003800David Osher0.7423.010.03287.0Clay12.8113.25YesNoNoWaterwaysYesWealthyNo06/04/1905/31/19Davidmouth, CA 37824-422.42
8fffe3800330032003900Arnold Reel0.006.04.0108.0Clay3.9819.76YesNoNoWaterwaysYesWorking ClassYes08/27/1808/23/18Lisaville, ND 43925-160.10
9fffe3800310031003800James Comfort0.1223.09.0195226.0Marble245.6349.25NoNoNoRoadwaysNoWorking ClassNo07/14/1607/10/16Jacobland, WV 85997-834.27
Customer IdArtist NameArtist ReputationHeightWidthWeightMaterialPrice Of SculptureBase Shipping PriceInternationalExpress ShipmentInstallation IncludedTransportFragileCustomer InformationRemote LocationScheduled DateDelivery DateCustomer LocationCost
6490fffe3700330035003000Charles Jones0.5215.08.097958.0Stone245.8528.59NoNoNoRoadwaysNoWorking ClassNo02/28/1502/26/15Riceburgh, DC 876721075.95
6491fffe3800300035003500Berry Simmons0.6713.08.0475.0Aluminium3.6512.93YesNoYesRoadwaysNoWorking ClassNo10/28/1810/27/18Jessicachester, MA 84581294.62
6492fffe3800350039003100Tiffanie Moreno0.8017.012.0424586.0Stone1648.1461.13NoNoYesRoadwaysNoWorking ClassNo09/12/1509/14/15Murphystad, MO 179415177.64
6493fffe350037003800William CurryNaN13.08.0154.0Wood5.3313.00NoYesNoAirwaysNoWealthyNo03/16/1703/16/17Jerryland, CO 24340174.57
6494fffe3300370030003500Karen Bayles0.45NaN28.010851.0Aluminium30.8367.70NoNoYesRoadwaysNoWorking ClassNo04/19/1604/21/16Singletonstad, MD 793031076.85
6495fffe3800370037003300Jeffrey Freudenthal0.3737.010.016551.0Brass28.2838.46YesYesNoAirwaysNoWealthyYes03/28/1803/25/18New Robert, VT 85335872.43
6496fffe310036003400Larry Edwards0.6715.0NaN18981.0NaN67.1827.72NoNoNoRoadwaysNoWorking ClassNo08/29/1508/27/15New Joshua, VA 357661347.02
6497fffe3600300031003300Denise Worth0.6819.08.0587.0Clay6.9210.38YesNoNoNaNYesWealthyNo04/10/1904/09/19Lake Kelly, MA 80823354.55
6498fffe3600350035003900Daniel Drew0.0233.09.01269377.0Stone2929.1369.76NoYesNoRoadwaysNoWorking ClassYes03/10/1903/12/19Hintonberg, UT 350065037.50
6499fffe3700310031003600Vernon Carroll0.1830.013.034729.0Brass46.6378.25NoYesNoAirwaysNoWorking ClassNo12/03/1612/05/16New Christopher, AK 87406722.47